A Replication and Review: Female sociality and sexual conflict shape offspring survival in a Neotropical primate

Hi reader,

I am about to walk you through a paper entitled Female sociality and sexual conflict shape offspring survival in a Neotropical primate by Kalbitzer et al. (2017). The reason I chose this paper is because it is a study of the Capuchin monkeys who made me fall in love with primatology. On the particularly hard days, they are what remind me why I am here.

Zoe with one of the Stars of the Paper

Before we begin, lets install the following packages: lme4

#Here is the code to install lme4.
install.packages("lme4",
   repos=c("http://lme4.r-forge.r-project.org/repos",
      getOption("repos")[["CRAN"]]))

A Brief Introduction

In a 2017 paper by Kalbitzer et al., researchers studying wild white-faced Capuchin monkeys living in Santa Rosa, Costa Rica set out to answer the question “what is the relationship between female sociality, offspring survival, and infanticide risk”?

Their study is based upon the knowledge that most mammalian species live in social groups and they may vary in degrees of sociality, which yield differential fitness. A few studies published prior to this one argued that female sociality has a positive impact on infant survival. However, no studies looked at the role that male reproductive strategies, notably infanticide, might influence the effects that female sociality has on infant survival.

We know that the reproductive strategies of males and females differ and that this may result in sexual conflict. We know too that infanticide significantly increases during periods of alpha male replacements.

In the tables and figures that follow, I will illustrate some of the key findings from their work:

1. Offspring of highly social and high ranking females had higher survivorship during stable periods.

2. Offspring of highly social and high ranking females were more likely to die/disappear during periods of alpha male replacements.

• This is likely because new alpha males move to the center of the group, where highly social females are located.

Therefore it is clear that female sociality may sometimes have negative fitness consequences due to male behavior.

Working with the Data

We begin by downloading the data, so that we may work with it.

library(curl)
f <- curl("https://raw.githubusercontent.com/ZoeEAlbert/zalbert-data-replication-assignment/main/Kalbitzer_et_al_capuchin_sociality_dyadic_data.csv")
d <- read.csv(f, header = TRUE, sep = ",", stringsAsFactors = FALSE)
head(d) #This will help us see the top 6 lines of every row. 

##   Group_ID Dyad_ID IndividualA_ID IndividualB_ID Year   DSI RValue_Dyad
## 1        1       1              2              1 2010 1.244       0.062
## 2        1       1              2              1 2011 2.602       0.062
## 3        1       2              8              1 2010 1.387       0.000
## 4        1       2              8              1 2011 1.531       0.000
## 5        1       3             11              1 2010 1.336       0.000
## 6        1       3             11              1 2011 1.298       0.000
##   KinshipCategory_Dyad AgeDifference_years_Dyad DominanceRank_IndividualA
## 1         maternal sib                      2.0                       0.4
## 2         maternal sib                      2.0                       0.3
## 3     maternal non-kin                      4.9                       0.7
## 4     maternal non-kin                      4.9                       0.7
## 5     maternal non-kin                      3.0                       0.6
## 6     maternal non-kin                      3.0                       0.5
##   DominanceRank_IndB Centrality_IndividualA Centrality_IndividualB
## 1                  0                  0.742                  0.705
## 2                  0                  0.838                  0.866
## 3                  0                  0.845                  0.705
## 4                  0                  0.887                  0.866
## 5                  0                  0.758                  0.705
## 6                  0                  0.875                  0.866
##   PresenceMaleInfant_IndividualA PresenceFemaleInfant_IndividualA
## 1                              0                                1
## 2                              0                                0
## 3                              0                                0
## 4                              0                                0
## 5                              0                                0
## 6                              0                                0
##   PresenceMaleInfant_IndividualB PresenceFemaleInfant_IndividualB
## 1                              0                                0
## 2                              0                                0
## 3                              0                                0
## 4                              0                                0
## 5                              0                                0
## 6                              0                                0

names(d) #Having the names spelled out like this will help us with our model later!

##  [1] "Group_ID"                         "Dyad_ID"                         
##  [3] "IndividualA_ID"                   "IndividualB_ID"                  
##  [5] "Year"                             "DSI"                             
##  [7] "RValue_Dyad"                      "KinshipCategory_Dyad"            
##  [9] "AgeDifference_years_Dyad"         "DominanceRank_IndividualA"       
## [11] "DominanceRank_IndB"               "Centrality_IndividualA"          
## [13] "Centrality_IndividualB"           "PresenceMaleInfant_IndividualA"  
## [15] "PresenceFemaleInfant_IndividualA" "PresenceMaleInfant_IndividualB"  
## [17] "PresenceFemaleInfant_IndividualB"

Female social bonds vary between and within dyads. The coefficient of variation was 86% between dyads and 67% within the same dyad over time. The variation in bond strength both within and between dyads is significantly related to three of the tested variables;

1. Relatedness (R-Value)

2. Dominance rank difference

3. Presence of female infants

Predictors of Dyadic Bond Strength

The first thing we are going to do is replicate the first table in the paper. Table 1 illustrates the following predictors of dyadic bond strength: R-value, Age difference, Rank difference, Male infant present, Female infant present.

df <-read.delim2("https://raw.githubusercontent.com/ZoeEAlbert/zalbert-data-replication-assignment/main/Kalbitzer_et_al_capuchin_sociality_dyadic_data.csv") #Let's first get our data into a data frame.

library(lme4) #Now, let's go to the library to get the package (lme4) that we need. 

lme <- lmer(data = df, RValue_Dyad ~ AgeDifference_years_Dyad + PresenceMaleInfant_IndividualA)

summary(lme)

coefficients(lme)

full <- lmer(data = d, duration ~ reprocondition + parity + (1 + reprocondition | subject) + (1 + parity | subject), REML = FALSE)

reduced <- lmer(data = d, duration ~ parity + (1 + reprocondition | subject) + (1 + parity | subject), REML = FALSE)

anova(reduced, full, test = “Chisq”)

As a point of reference, here is what it should look like, based upon the paper.

Stability of Bonds with Top Three Partners

This next figure is a histogram measuring Partner Stability Index by frequency. {r- Figure S2 .}

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